LanceDB: vector database, search, AI

podcast

Open source, on-disk vector search with LanceDB featuring Chang She, CEO and co-founder of LanceDB (Practical AI #250) |> Changelog

LanceDB

Episode #234 “Vector DBs beyond the hype”

Vector databases blog posts from Prashanth:

• (Part 1): What makes each one different?
• (Part 2): Understanding their internals
• (Part 3): Not all indexes are created equal

lancedb/lancedb: Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps! @GitHub

Rust + Python + TypeScript

Apache

Master Vector Databases | Udemy

Vector Databases: A Beginner’s Guide! | by Pavan Belagatti | Data And Beyond | Medium

What is a Vector Database & How Does it Work? Use Cases + Examples | Pinecone

Vector Database | Microsoft Learn

A vector database is a type of database that stores data as high-dimensional vectors, which are mathematical representations of features or attributes. Each vector has a certain number of dimensions, which can range from tens to thousands, depending on the complexity and granularity of the data. The vectors are usually generated by applying some kind of transformation or embedding function to the raw data, such as text, images, audio, video, and others. The embedding function can be based on various methods, such as machine learning models, word embeddings, feature extraction algorithms.

The main advantage of a vector database is that it allows for fast and accurate similarity search and retrieval of data based on their vector distance or similarity. This means that instead of using traditional methods of querying databases based on exact matches or predefined criteria, you can use a vector database to find the most similar or relevant data based on their semantic or contextual meaning.